Model uses satellite imagery to assess road quality.
problem Inefficient monitoring of road investments in developing regions.
method Convolutional neural networks on high-resolution satellite imagery.
result Models achieve 88% binary and 73% 5-category classification accuracy.
Machine learning detects road anomalies and aggressive driving from smartphone data.
problem Road quality assessment and aggressive driving detection.
method Machine learning techniques applied to smartphone acceleration data.
result Robust platform for road transport evaluation.
Owing to the expeditious growth in the information and communication technologies, smart cities have raised the expectations in terms of efficient functioning and management. One key aspect of residents' daily comfort is assured through affording reliable traffic management and route planning. Comprehensively, the majo…
Generative model extracts road networks from images.
problem Extracting road networks from image data.
method Developed Generative Graph Transformer (GGT) for image-conditioned graph generation.
result Demonstrated effective road network generation from semantic segmentation data.
Network embedding helps predict speed limits on incomplete Danish road network.
problem Incomplete speed limit data on Danish roads limits machine learning applications.
method Applied node2vec network embedding to Danish road network.
result Network embedding can derive useful features for predicting speed limits.
Road Network Metric Learning improves ETA prediction accuracy by addressing data sparsity.
problem Data sparsity in road network embedding vectors affects ETA prediction accuracy.
method Proposes Road Network Metric Learning (RNML-ETA) framework with an auxiliary metric learning task and triangle loss.
result RNML-ETA outperforms state-of-the-art models and improves prediction accuracy for cold links.
New methods detect roads in low-res satellite data, overcoming visibility challenges.
problem Detecting roads in low-resolution satellite imagery, especially those hard to see.
method Two deep learning frameworks for ordinal classification of road types from satellite time series data.
result Models can identify large and medium-sized roads from Sentinel-2 imagery.
GCNs adapted for road networks improve performance on edge prediction tasks.
problem Improving machine learning on road networks for edge prediction tasks.
method Introducing Relational Fusion Network (RFN) for road networks.
result RFN outperforms state-of-the-art GCNs on road segment regression and classification tasks.
RFN improves GCNs for road networks, outperforming state-of-the-art by 21%-40%.
problem Leveraging the structure of road networks effectively in machine learning tasks.
method Introducing RFN, a novel GCN specifically designed for road networks.
result RFN outperforms state-of-the-art GCNs by 21%-40% on road network tasks.
System predicts ice formation to improve road safety.
problem Improving road safety through accurate ice formation prediction.
method Combining physical models and machine learning for data correction.
result Machine learning effectively corrects incorrect data in forecasts.
Paper presents a self-supervised method to infer road lane networks.
problem Difficult and costly to create lane maps for autonomous vehicles.
method Self-supervised learning using neural and search-based model.
result Model can generalize to new road layouts, unlike previous approaches.
Study proposes GRU-D networks for missing value handling in road surface friction prediction.
problem Missing values in road surface friction data affect prediction accuracy.
method Gated Recurrent Unit (GRU) network with decay mechanism.
result GRU-D networks outperform baseline models in road surface friction prediction.
Deep learning maps road safety features from streetview imagery.
problem Accurate mapping of road safety features for safer roads.
method Proposes a deep learning approach with LSTM to capture spatial autocorrelation.
result Model outperforms baseline methods in mapping road safety features.
The UN Sustainable Development Goals allude to the importance of infrastructure quality in three of its seventeen goals. However, monitoring infrastructure quality in developing regions remains prohibitively expensive and impedes efforts to measure progress toward these goals. To this end, we investigate the use of wid…
Model predicts road vehicle accidents with high resolution.
problem Accidents on roads in Montreal.
method Big data analytics, machine learning (Balanced Random Forest algorithm).
result 85% of road vehicle collisions detected with 13% false positives.
Automated road infrastructure mapping using connected vehicle data and deep learning.
problem Manual identification of intersections is laborious and time-consuming.
method Geohashing, YOLOv5 algorithm for classification of road segments and intersections.
result Overall classification accuracy of 95%, with high F1 scores for straight roads and intersections.
Engine predicts real-time air quality with high resolution.
problem Real-time prediction of air pollutants for health monitoring.
method Combines official data, models, land cover, traffic data for high-resolution predictions.
result Engine produces predictions with resolution of a few dozen meters.
Game theory improves smart road sign security against small perturbations.
problem Ensuring smart road signs are secure from small-scale adversarial attacks.
method Integrates game theory into smart road sign classification to detect imperceptible perturbations.
result Proposes a randomized detection strategy to ensure robustness against worst-case attackers.
Study improves prediction of UK road accidents' severity using AI.
problem Improving prediction of UK road traffic accident severity.
method Combination of machine learning, econometric, and statistical methods on historical data.
result XGBoost model with RMSE of 0.176 and MAE of 0.087 outperforms naive forecasting.
Enhanced travel time prediction using deep neural networks and road network information.
problem Improving travel time estimation using deep learning models.
method Proposes incorporating road network information into deep learning models for travel time prediction.
result Improved travel time prediction, especially with limited training data.
Deep learning generates addresses for under-addressed cities.
problem Lack of street addresses in developing countries.
method Deep learning applied to satellite images to generate addresses.
result Algorithm generates addresses for neighborhoods and regions.
Study shows 'Belt and Road' node cities boost digital finance in China.
problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.
Increasing availability of vehicle GPS data has created potentially transformative opportunities for traffic management, route planning and other location-based services. Critical to the utility of the data is their accuracy. Map-matching is the process of improving the accuracy by aligning GPS data with the road netwo…
3D-TGCN learns road graphs from time series similarity for spatio-temporal traffic forecasting.
problem Challenging spatio-temporal prediction in traffic networks due to dependency and dynamics.
method Proposes 3D-TGCN with novel components: spatial information-free road graph and 3D graph convolution.
result 3D-TGCN outperforms state-of-the-art baselines in traffic forecasting.
Study improves traffic prediction intervals for minor roads.
problem Uncertainty in traffic data for underrepresented minor roads.
method Quantile Random Forest with PCA for interval prediction.
result Achieved 88.22% interval coverage and Winkler Score of 7,468.47.
Develops a deep clustering framework for large-scale road traffic prediction.
problem Challenges in modeling diverse traffic patterns and handling high-dimensional time series with low latency.
method Combines deep clustering with CNNs and RNNs to predict road traffic at large-scale networks.
result The DeepCluster framework effectively clusters road segments and improves prediction performance.
China's rapid economic growth resulted in serious air pollution, which caused substantial losses to economic development and residents' health. In particular, the road transport sector has been blamed to be one of the major emitters. During the past decades, fluctuation in the international oil prices has imposed signi…
Model predicts road traffic using high-dimensional time-series with L1-penalization.
problem Predicting high-dimensional road traffic data with limited observations.
method Vector autoregressive model with L1-penalization for high-dimensional regression.
result The approach identifies the most important road sections and is competitive in prediction.
NL-LinkNet improves road extraction from satellite images with fewer parameters and faster training.
problem Efficiently extracting roads from high-resolution satellite images.
method Integrates non-local blocks into LinkNet for better feature context.
result NL-LinkNet outperformed state-of-the-art models in the DeepGlobe Challenge.
We provide a model to understand how adverse weather conditions modify traffic flow dynamic. We first prove that the microscopic Free Flow Speed of the vehicles is changed and then provide a rule to model this change. For this, we consider a thresholded linear model, corresponding to an application of a MARS model to r…
Improved road segmentation on low-res LIDAR data for autonomous vehicles.
problem Low-resolution LIDAR data affects road segmentation accuracy in autonomous vehicles.
method Subsampled LIDAR data transformation into feature maps, use local normal vector with spherical coordinates.
result Improves road segmentation accuracy on low-resolution LIDAR data.
Proposes a method to find traffic regulations efficiently.
problem Finding appropriate traffic regulations in congested events.
method Graph Convolutional Networks for modeling regulation effects.
result The method can find a road to close that reduces travel time.
Paper proposes a method to improve autonomous vehicle performance using synthetically generated images.
problem Limited access to real-world datasets for autonomous vehicle training in countries with scarce data.
method Synthetically generated images to augment and train neural networks on small datasets.
result About 10% improvement in model performance observed.
Modern vehicles are equipped with increasingly complex sensors. These sensors generate large volumes of data that provide opportunities for modeling and analysis. Here, we are interested in exploiting this data to learn aspects of behaviors and the road network associated with individual drivers. Our dataset is collect…
In this paper, we train a recurrent neural network to learn dynamics of a chaotic road environment and to project the future of the environment on an image. Future projection can be used to anticipate an unseen environment for example, in autonomous driving. Road environment is highly dynamic and complex due to the int…
Study uses Android smartphones to measure road roughness, finds ML better than RMS.
problem Measuring road roughness using smartphones.
method Three smartphones, Android OS, RMS and ML methods, comparison with IRI.
result ML method outperforms RMS in detecting road roughness.
Wisard neural network detects cracks in asphalt roads.
problem Automatic detection of cracks in paved roads.
method Weightless neural network (Wisard) with transfer learning.
result 85.71% accuracy in detecting cracks.
ROAD-EnKFs use learned low-dimensional models to improve state reconstruction and forecasting.
problem Reconstructing and forecasting states of unknown or expensive systems.
method Learned low-dimensional surrogate models and ensemble Kalman filter integration.
result ROAD-EnKFs achieve higher accuracy at lower computational cost than existing methods.
Polarimetric images enhance object detection in adverse weather conditions.
problem Object detection in road scenes is challenging in adverse weather conditions.
method Combining polarimetric imaging and deep learning.
result Polarimetry improves object detection by 20% to 50% compared to conventional RGB images.
IG-RL learns adaptive traffic signals for any network, outperforming existing methods.
problem Adaptive traffic signal control for large networks with combinatorial state and action spaces.
method Graph-Convolutional Networks for decentralized, flexible control.
result IG-RL generalizes to new networks and traffic conditions without additional training.
We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …
The paper uses CNNs to classify road conditions in real-time.
problem Classifying road conditions in real-time using images from cameras across North America.
method Used state-of-the-art convolutional neural networks (VGG-16, ResNet50, Xception, InceptionResNetV2, EfficientNet-B0 and EfficientNet-B4) to classify images by road condition.
result EfficientNet-B4 framework achieved validation accuracy of 90.6% in real-time map building.
Autonomous driving presents one of the largest problems that the robotics and artificial intelligence communities are facing at the moment, both in terms of difficulty and potential societal impact. Self-driving vehicles (SDVs) are expected to prevent road accidents and save millions of lives while improving the liveli…
Paper proposes machine learning models for more accurate road inspection.
problem Traditional road inspection systems have safety, energy, and cost issues.
method Hybrid machine learning models using surface deflection data from FWD tests.
result CMIS model outperforms other models with APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajec…
In this paper, the problem of road friction prediction from a fleet of connected vehicles is investigated. A framework is proposed to predict the road friction level using both historical friction data from the connected cars and data from weather stations, and comparative results from different methods are presented. …
For high-dimensional classification, it is well known that naively performing the Fisher discriminant rule leads to poor results due to diverging spectra and noise accumulation. Therefore, researchers proposed independence rules to circumvent the diverse spectra, and sparse independence rules to mitigate the issue of n…
This work trains a model to predict human driving directions from road scenes.
problem Defining implicit rules of human behavior for autonomous vehicles.
method Self-supervised learning of probabilistic network model.
result Model successfully generalizes to new road scenes.